Object processing employing movement

ABSTRACT

Directional albedo of a particular article, such as an identity card, is measured and stored. When the article is later presented, it can be confirmed to be the same particular article by re-measuring the albedo function, and checking for correspondence against the earlier-stored data. The re-measuring can be performed through us of a handheld optical device, such as a camera-equipped cell phone. The albedo function can serve as random key data in a variety of cryptographic applications. The function can be changed during the life of the article. A variety of other features are also detailed.

RELATED APPLICATION DATA

This application claims priority benefit to provisional application60/762,055, filed Jan. 23, 2006, and to provisional application60/866,033, filed Nov. 15, 2006.

Two other applications, with essentially the same specification, werefiled by the present assignee on the same date as this application.Those other applications are entitled Methods and Cards EmployingOptical Phenomena; and Capturing Physical Feature Data.

FIELD

The technology detailed herein relates—in certain aspects—to sensingoptical data from an object from plural vantage points, and uses of theresulting data.

BACKGROUND

The following references detail technologies applicable in connectionwith applicants' work.

U.S. Pat. No. 6,584,214 discloses how three-dimensional characteristicsof a complex physical structure can be used to generate a uniqueidentifier useful, e.g., in cryptography. In effect, the physicalcharacteristics represent the basis of a “physical one-way hashfunction” that facilitates derivation of an identifier based on thestructure (yet the structure cannot be reproduced given only theidentifier).

Related work is detailed in the March, 2001, MIT thesis by Pappu,entitled “Physical One-Way Functions,” and in the related Pappu et alpaper of the same name, published in the Sep. 20, 2002, issue of Science(Vol. 297, pp. 2026-2030, attached hereto as Exhibit A).

Chen et al have noted that an inexpensive physical object can serve as acryptographic element, if a random unique structure of the object (e.g.,paper fiber) is accurately quantified. (“Certifying Authenticity viaFiber-Infused Paper,” ACM SIGecom Exchanges, Volume 5, Issue 3, April2005, pages 29-37, attached hereto as Exhibit B.)

Rodriguez et al have written about use of cell phones and like devicesfor validation of document security features. (“On the Use of MobileImaging Devices for the Validation of First- and Second-Line SecurityFeatures,” SPIE Vol. 6075, February, 2006, attached as Exhibit C.)

WIPO patent publication WO 2005/106783 details how the propagation ofsonic vibrations through an inhomogeneous medium—such as a card withembedded irregularities—can generate data by which the medium can beuniquely identified.

A number of patent documents teach how a medium can be uniquelyidentified by reference to its inherent physical characteristics, suchmicroscopic grain structure, optical characteristics, or structuralcharacteristics. Examples include US20050190914, US20050210255,US20030035564, US20050262350, WO0065541, WO03030105 (corresponding,e.g., to U.S. applications 60/317,665, and 60/394,914), and WO03087991(corresponding, e.g., to 60/371,073).

Arrangements in which data is represented by reference to angles (e.g.,angular symbologies) are taught, e.g., in US2003026448 andUS20050285761.

U.S. Pat. No. 6,987,568 details a method and apparatus for measuringbidirectional reflectance distribution function.

U.S. Pat. No. 6,421,453 shows that gestures can be employed inidentification applications.

To provide a comprehensive disclosure without unduly lengthening thisspecification, the documents identified herein (both above and below)are incorporated by reference.

DISCUSSION

The term “secure document” conjures various concepts to the artisan,generally characterized by expensive production materials and machinery.Examples include currency formed on commercially unobtainable paper andintaglio-engraved with elaborate guilloche patterns, and driver licensesincorporating sophisticated laminates and myriad otheranti-counterfeiting technologies.

More generally, however, a secure document is simply one thatessentially cannot be duplicated.

Contrary to familiar notions, in one sense all documents are secure. Atan atomic level, no document can be “duplicated.” If, e.g., an originaldriver license could be atomically characterized at the time of itsissuance, and the resulting massive data set stored, then this storeddata could later be used as a reference to determine whether a suspectlicense is the original one, or an imperfect forgery.

A system built on such principles is, of course, impractical. One hurdleis to characterize the license—at the time of its issuance—at the atomiclevel. If such equipment existed, it would be extraordinarily expensive.A second hurdle is more confounding: similar equipment would have to beinstalled at every reader location (retail outlet, airline check-in,police cruiser, etc) at which authenticity of the license is to beassessed.

However, the insight that every document (indeed, every tangiblearticle) is irreproducible at some level, allows for some interestinginquiries.

For example, how much data must be collected from an article to permitit to be distinguished from seemingly identical articles (e.g., articlesproduced sequentially using the same manufacturing equipment and usingsame source of raw materials)? Can sufficient data be collectedoptically, or is resort to characterizing other physical properties(chemical composition, mechanical features) required?

Consider an ID card, measuring 3.5″×2.″ If optically scanned at the timeof its issuance using a 600 dpi scanner, it produces 360,000 samplesover each square inch. If each sample is composed of 12 bits of redinformation, 12 bits of blue information, and 12 bits of greeninformation, the scanning process yield 12,960,000 bits for each squareinch, or 90,720,000 bits across the face of the card. This data could bestored and used as a check to determine whether a suspect card is theoriginal. Yet experience suggests that this nearly 100 megabit data setis not sufficiently detailed for such card authentication. Acounterfeiter with such a scanner and a decent printer could produce aforged card that cannot be reliably distinguished from the original(using traditional techniques) by reference to this 100 megabit data set(taking into account a margin of natural variability associated withscanner noise and other factors, i.e., the same scanner, scanning thesame article twice in succession, does not produce two identical datasets, e.g., due to shot noise and other phenomena; ultimately, aformalized Bayesian decision and/or digital hash comparison process canbetter define the word “distinguish” in a practical setting, but for thepurposes of this general introduction, this word is sufficient).

Higher resolution scanning might be employed to generate a still largerset of characterization data, but the associated costs of deploying highresolution scanners to a large number of reading stations soon makessuch approaches impractical. Moreover, as scanning resolution isincreased, it becomes increasingly difficult to determine whether adifference in data sets is due to different cards, or something assimple as scanner noise.

Thus, flat-scan optical characterization of the spectral density of acard or document does not appear sufficient; resort to other physicalproperties—and their precise characterization would appear to berequired.

Or so it would seem.

Actually, the desired results may be achieved by counter-intuitiveapproaches. For example, instead of looking more closely at a suspectcard—look at it from further away. Likewise, instead of examining thecard under tightly controlled measurement conditions, sense it in alargely uncontrolled environment. And, to top things off, use a simpleoptical sensor. (What first appears like a recipe for disaster mightinstead be the seeds for success.)

In accordance with one aspect of the technology detailed herein, asimple optical sensor is used to capture sufficient data from a card touniquely distinguish the card from another, even if both cards aredesigned to be identical, and are produced sequentially from the samemachine.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an ID card, and an associated geometrical reference system(comprising x, y, z, tip angle, tilt angle, and rotation angle).

FIG. 2 shows the card of FIG. 1, with another geometrical referencesystem (x, y, z, wobble angle, and azimuth angle), and showing how acentroid of reflection for different a-pels on the surface of card isnot always oriented along the z-axis, but rather typically wobbles,e.g., over a range of 0-20 degrees, and over a different azimuth angles.

FIG. 3 is a schematic section view (passing through depicted y-axis inFIG. 1) showing part of an apparatus 20 for capturing card image datafrom different directions, at the time of card production.

FIG. 4 is a block diagram of apparatus 20.

FIG. 5 is a plot showing the intensity from a sample a-pel as measuredat different viewing angles.

FIG. 6 shows different reflectivity functions.

FIG. 7 is a flow chart outlining an illustrative technique forcharacterizing a card's 2D albedo map at the time of card production.

FIG. 8 is a block diagram of a reader station 30, with a card beingwaved in front of a webcam.

FIGS. 9A-C, and 10A-C, show successive frames of how a card might beviewed by an optical sensor at a reader station, when the card is wavedbefore the sensor by a user.

FIG. 11 is a flow chart outline one illustrative technique forestimating a card's 2D albedo map at a reader station.

FIGS. 12A and 12B show plots detailing a “wave” of a card in front of aweb cam sensor.

FIG. 13 shows how microdroplets of thermoplastic resin on a driverlicense laminate may be heated by an obliquely applied laser source,applied from different directions, to reshape the laminate surface, andthus the license's albedo function.

DETAILED DESCRIPTION

For expository convenience, the following specification focuses ondriver licenses. However, it should be understand that the principlesherein can be used with tangible articles of any time (e.g., passports,paper currency, birth certificates, legal documents, medical records,computer storage media, etc.).

FIG. 1 shows the top face of a driver license 10, and one geometricalframe of reference with which certain of the features detailed below maybe described.

Also shown in FIG. 1, in the lower left corner, are “a-pels” 12 a, 12 b,12 c (“albedo picture elements”) that may be imagined as extendingacross the face of the card. These a-pels each correspond to an excerptof the card face as sensed by an imaging system. (For clarity's sake,the a-pels are not to scale. They might more realistically be on theorder of 0.1 or 1.0 millimeters on a side, or somewhere under 1,000 toover 100,000 a-pels per square inch of card surface.)

In a gross sense, generally flat surfaces typically exhibit a Lambertianreflectivity profile as a function of viewing angle toward that surface.That is, the maximum reflection of light from the surface occurs alongthe axis perpendicular to the surface (i.e., axis z in FIG. 1). However,if examined in more detail (e.g., on a per a-pel basis), it is foundthat the angle of maximum reflectivity typically diverges somewhat fromthis ideal. This divergence—shown as a “wobble” angle in FIG. 2, may beon the order of a few tenths of a degree in certain materials, but onthe order of several degrees, or several tens of degrees, in othermaterials.

This direction at which light maximally reflects from an a-pel may becharacterized by the wobble angle (i.e., the divergence from the zaxis), and also by azimuth. Azimuth—measured within the plane of thecard—may be regarded as the direction towards which themaximally-reflected light “leans.”

In FIG. 2, the direction of maximum reflectivity for each a-pel is shownby a bold vector (arrow) 11. The grey arrow 13 beneath is a projectionof the vector 11 onto the card's surface, and indicates the azimuthangle for each vector. As can be seen, the reflectivity vectors 11associated with different a-pels in FIG. 2 have generally random wobbleand azimuth angles.

Collectively, the reflectivity vectors 11 shown in FIG. 2 areessentially unique to any item. Like a fingerprint, they can be used tocharacterize the item, and distinguish it from all others (even “copies”that appear on close inspection—using classic flat-bed scanning orsingle-direction viewing—to be identical).

In addition to having wobble and azimuth angles, each of the vectors 11in FIG. 2 is also characterized by length. The length of each vectorindicates the magnitude of light reflected from a corresponding a-pel.The magnitude of reflected light can be a function of several factors.One prominent factor is the color of the surface: an a-pel that issubstantially white reflects more light than a a-pel that issubstantially black. When a flatbed scanner, or a camera, images anobject, the pixel data that it captures, generally speaking, is an arrayof a-pel magnitude data.

A scanner or camera does not capture data from which, e.g., wobble orazimuth angles can be determined. Thus, in optically characterizing acard, a scanner captures only one dimension of data: magnitude data. Twofurther dimensions of independent data—wobble angle and azimuthangle—are ignored. By paying attention to these further dimensions ofdata, exponentially-improved abilities to identify an item—anddistinguish it from others—are achieved. (A three-dimensional cylinder,viewed in only two dimensions, may appear as a rectangle, a circle, anellipse, or a more complex shape—depending on the two-dimensional plane.Such ambiguities are easily resolved by increasing the dimension by one.Here the dimension can be increased by two.)

A first task, then, is to capture the multi-dimensional data thatcharacterizes the card. FIG. 3 shows part of an apparatus 20 for doingso.

Apparatus 20 comprises an array of cameras 14 disposed above a card 10.The card may be placed on a stage, or it may be held in position by apick-and-place robot system.

Each camera 14 includes a lens 16, and a 2D image sensor 18. The imagesensors may comprise, e.g., 1-5 megapixel CCD or CMOS sensors, as arecustomarily used in digital cameras.

The cameras are spaced at known locations relative to the card. In thesectional view of FIG. 3, seven cameras, 14 a-14 g, are shown—eachpositioned in the y-z plane of the card, at 10 degree spacings.Additional cameras (not shown) may be positioned in the x-z plane of thecard, with similar angular spacings.

Desirably, images of the card are captured from a variety ofperspectives. Basically, the idea here is to sample the reflectivityfunction of each a-pel on the card from a number of differentdirections, and use the sampled data points to determine (i.e.,estimate) the approximate wobble and azimuth angle at which reflectivityis maximum. The resulting data may be regarded as the 2D(wobble/azimuth) albedo function across the card. (Note: the scientificliterature tends to explicitly add the phrase “bi-reflectance” or“bi-directional” to the word “albedo”; most of this disclosure willimplicitly include this directional aspect of the word “albedo”.)

The FIG. 3 arrangement may comprise an array of 15 cameras, in an “X”configuration, each placed along a hemispherical surface over the card.Or the depicted arrangement may comprise 49 cameras, in a 7×7 array,warped to fit over the hemispherical surface. Lesser (or greater)numbers of cameras can alternatively be used (e.g., “X” patternsemploying 5 or 10 cameras, or square arrays of 9 or 16 cameras). Aminimal arrangement may comprise just three or four cameras, e.g., eachviewing the card from an oblique angle of 15 degrees, and spaced every120 or 90 degrees, respectively, around the object.

It is not necessary that the cameras all be equi-distant from the card.Nor is the spacing critical. In typical arrangements, lens-to-carddistances on the order of 3″-30″ inches may be used, although greaterand lesser distances are also possible. (Especially when the card isimaged from short distances, compensation for parallax effects may bedesirable. For example, the viewing angle for camera 14 g may not be 30degrees for all a-pels across the card. However, this effect is easilydetermined and can be taken into account when determining the wobble andazimuth angles.)

Nor is it required that the cameras be disposed in a regular array. Someadvantages can accrue by stochastic sampling, i.e., by sampling fromrandom directions.

In actual practice, cost and mechanical considerations may dictate thata lesser number of cameras be used. In one alternative, a single camerais used, in conjunction with an array of mirrors. Either the camera, orthe mirror system, is moved as necessary to capture a sequence ofdifferent card images—each from a different direction.

Yet another arrangement is to position the card on a tip/tilt table,beneath a single camera. The card can be sequentially moved to a numberof different positions relative to the camera, and an image is thenacquired from each different card-camera presentation angle.

FIG. 3 does not show an illumination source, and the particularillumination source used is a secondary matter (i.e., of signal-to-noiseratios on obtaining wobble/azimuth signature data), but not of primaryconcern, where a variety of light sources should all suffice. Ordinaryoffice lighting can potentially suffice—provided care is taken that thecamera systems do not shadow the card and produce measurement-systemartifacts. Or the apparatus 20 can include one or more controlled lightsources. Generally, lighting from above the card surface is desired.Diffuse lighting can be used, but may tend to blur the directionalreflectivity of a-pels on the card surface and tend to reduce the wobbleamplitude of the resultant wobble peaks.

In some arrangements, polarized light, and/or polarizing filters at thesensors, can be used to further characterize the card's albedo function.Similarly, the albedo function may be sampled at different wavelengthsof light. Both of these approaches can provide significant practicalextensions of the basic principles of this disclosure, but they are notnecessary for basic enablement.

FIG. 4 shows the magnitude of light reflected from a particular a-pel 12a on the card, as sensed by cameras 14 a-14 g, at respective angles of−30, −20, −10, 0, 10, 20, and 30 degrees along the y-z plane.

Light reflected from a given ‘pel’ may be imaged onto a 3×3 patch ofpixels in directly-overhead camera 14 g, but may be imaged onto only 2×3patches of pixels in cameras 14 a and 14 g. Intervening cameras 14 b, 14c, 14 e, and 14 f may have fractional rows/columns of photosensorsilluminated by light reflected from the a-pel. With knowledge of the CCDlayout (e.g., the dimensions of each component photosensor, and theborder between photosensors), and the positioning of the cameras, sucheffects (e.g., fractional illumination) can be compensated-for (e.g., byweighting the contributions from different photosensors differently inaggregating the net illumination reflected from an a-pel. The aggregateillumination from an a-pel may thus range in value from zero to 2295(the latter being a full 8 bit signal of 255, summed across 9fully-illumined pixels).) For convenience of notation, this aggregate isrepresented in FIG. 4 on a scale of 0-100.

From inspection (i.e., by imagining a curve connecting the depictedsample points), it appears that the reflectivity function from samplea-pel 12 a has a peak at about 6 degrees. However, the curve defined byFIG. 4 is just one slice through the reflectivity function's 3D shape(wobble/azimuth/magnitude). Other cameras—viewing the a-pel frompositions off the axis of cameras 14 a-14 g, are needed to more fullycharacterize the a-pel's reflectivity function, or at the very least thegeneral location of the albedo peak. Even with just the data from FIG.4, however, we know that the reflectivity function “leans” towards thetop edge of the card. (Unknown, from this data, is whether it leans alsotowards the left or right edges of the card.)

Given sample data from a set of non-collinear viewpoints, a centroidalgorithm can be applied to mathematically determine a maxima of thea-pel's reflectivity function, in wobble angle, azimuth angle, andmagnitude. This process can be performed by the computer 15 of FIG. 4.(Computer 15 can also serve other roles, such as being the“decisionmaker” that adjudicates whether cards sensed by reader 30 aregenuine.)

A statistical analysis of the wobble angles from different a-pels acrossa card is expected to show a generally Gaussian distribution (thoughsignificant departures from true Gaussian should cause no problem, inany event), centered about zero degrees, and with a standard deviationon the order of between 1 and 15 degrees, depending on material.

In FIG. 3, the cameras span a range of angles, ±30 degrees, that islarger than the vast majority of wobble angles. Having at least onecamera on each side of an a-pel's wobble angle helps refine the accuracyby which the wobble angle can be determined (e.g., by the centroidalgorithm). However, this is not a requirement. For example, samplestaken from cameras at 0, 6 and 12 degrees can nonetheless allowestimation of a wobble angle of, e.g., 15 or 20 degrees.

When a driver license is manufactured, e.g., by equipment at a stateDepartment of Motor Vehicles (DMV) office, or at a central manufacturingfacility, the license desirably is characterized by an apparatus 20 likethat shown in FIGS. 3 and 4 prior to being issued to the owner (whichmay be by mailing, in the case of a central manufacturing facility). Insome processes, such apparatus can be included at the end of themanufacturing process. The resulting data is stored in the database 17of FIG. 4.

In one arrangement, the albedo data is stored as a series of records,each indexed by the a-pel's respective row and column number. If eacha-pel is 0.5 millimeter on a side, the albedo function for a driverlicense may comprise 100 rows by 175 columns of data, or 17,500 a-pelstotal. Each record may store the wobble angle for that a-pel, togetherwith the associated azimuth angle, and also the magnitude.

More or less data can, of course, be stored. For example, in somearrangements the magnitude data may not be stored. In another, eitherthe wobble angle or the azimuth angle may not be stored.

In still other arrangements, more data is stored. The albedo functionfor each a-pel may be described not just by the 3D coordinates of theendpoints of the vectors 11 shown in FIG. 2, but also by the 3D volumeof the reflectivity function. That is, the light reflected from an a-pelmay be narrowly concentrated along a vector 11 (like a spotlightfunction), or it may form a broad volume, with lots of spread about thevector (like a floodlight function). A slice of a spotlight-likereflectivity function volume is shown by the dashed curve of FIG. 6; aslice from a more floodlight-like reflectivity function volume is shownby the solid line.

In one arrangement, the raw data from all of the cameras is stored inthe database—characterizing the 3D volume reflectivity function atdifferent sample angles. In another arrangement, a curve fittingalgorithm is applied to estimate a 3D model of the reflectivity volumefrom the sample points, and the parameters of this model can then bestored. Furthermore, a low-order polynomial fit to the volume can beremoved from the data, leaving only the higher order “unique structure”as a very subtle form of characterizing the volumes. Such possibilitiestend to go beyond what mass-produced cards such as driver's licenses maycontemplate as a practical matter, and point more toward highersensitivity applications such as airport security and the like.

The database 17 in which the albedo data is stored can comprise theDMV's existing licensee database, e.g., including name, age, drivingrestrictions, photo portrait, etc. Or it can comprise a separatedatabase.

Driver licenses are typically encoded with machine-readable information,such as digital watermarks, bar codes and RFID data. The informationconveyed by the machine-readable data may also be stored in the databasewith the albedo measurements, together with other information, such as acard ID.

The exemplary card characterization process detailed above is set forthin the flow chart of FIG. 7.

After characterization, the license is issued to the user. It then goesinto the user's wallet or purse and begins a life of abuse—beingscraped, worn, washed, etc. Eventually, it is pulled from the wallet andpresented as an ID credential, at a reading station. (The readingstation may be at an airport security checkpoint, at a liquor store, ina police cruiser, at a building access, etc.)

Desirably, each reader station is relatively inexpensive, and does notrequire much training to operate. One version of a reader station 30(FIG. 8) is a conventional personal computer 34, equipped with a singlecamera 32 and a network connection 36.

The camera 32 need not be a carefully characterized measuringinstrument; a simple webcam will suffice. One popular web cam is theCreative “Live Cam Voice” model, which retails for less than $100, andhas a 1.3 megapixel sensor. Others include the Creative “WebCam Live!Ultra” model (which includes a 1024×768 sensor), and the Logitech“Quickcam Pro 4000” (which includes a 1280×960 pixel sensor). Thesewebcams all can capture 30 frames of video per second, at a resolutionof 640×480 pixels or higher.

To present a card 10 for reading, the user simply waves the card infront of the webcam (as shown by the wavy dashed line in FIG. 8, whichmay be termed a “swoop”). The webcam captures multiple frames of imagedata depicting the card, e.g., one every 0.033 seconds.

As the card moves across the webcam sensor's field of view, it presentsdifferent perspectives, i.e., the webcam captures frames of image datafrom different angles. Whereas in the card characterization apparatus 20of FIG. 3, plural cameras capture several perspectives of image datafrom a stationary card, in the reader arrangement 30 of FIG. 8, a singlecamera captures several perspectives of image data from a moving card.

The data acquired by reader station 30 does not compare—in quality—tothat captured by characterization apparatus 20. However, it isnonetheless more than sufficient—in conjunction with the earlieracquired information stored in database 17—to discriminate the card fromeven “perfect” counterfeits.

FIGS. 9A, 9B and 9C show a sample sequence of images that may becaptured by reader station webcam 32. (The center of the webcam's fieldof view is shown by the dotted +.) In FIG. 9A, the left edge of the cardis further away from the webcam, so appears fore-shortened. The card islikewise rotated a bit to the left. In FIG. 9B, the card is squarelypresented before the webcam. In FIG. 9C, the right edge of the card isfurther away from the webcam, and the card is rotated a bit to theright.

In FIG. 9B, a frame is captured with the card directly facing the camera(i.e., the card is oriented with its z-axis passing through the lens ofthe webcam). This is not necessary. As long as the front of the cardcomes within about 10 to 20 degrees of facing the camera—at some pointduring its travel—the card's 2D albedo function may be satisfactorilyestimated.

(It is not necessary that card be entirely within field of view in eachframe; useful data can be obtained even if only if part of the card isvisible.)

FIGS. 10A, 10B, and 10C show another sample sequence. Here the card isnot laterally moved past the camera. Instead, it is simply tilted todifferent orientations.

Because the card in FIG. 10 is moved about just a single axis (i.e., the“tilt” axis in FIG. 1), the image samples acquired by webcam 32 likewisefall along a common axis. Although the card's albedo function can beestimated with such data, a better estimate is obtained if the card ismoved around both the tip and tilt axis, as it is being waved in frontof the webcam.

When the card 10 was originally characterized by apparatus 20, themeasurements were taken in a precisely defined geometrical referenceframe, e.g., in which the card was located at a known position relativeto the cameras. The ‘wave’ of the card in front of webcam 32 does notenjoy this advantage. Nonetheless, the geometry of the ‘wave’ can stillbe precisely assessed. (Note: To be a bit more precise, the card will bepresented to the camera across a series of frames, with each frameoccupying a generally unique angular direction of the camera relative tothe perpendicular of the card, thus producing a form of “track” throughangular space, where from a consumer's or user's perspective waving thecard in front of the camera, the term “wave” is a bit more intuitive).

A watermark carried by the card can play a key role here. The preferredwatermark includes a steganographic calibration (e.g., reference orsubliminal grid) signal by which affine distortion of the imaged cardcan be accurately quantified. (Examples are given, e.g., in U.S. Pat.Nos. 6,614,914 and 6,580,809; in publications US US20040105569 andUS20040101157; U.S. Pat. No. 6,959,098 teaches how distortion can becharacterized by such watermark calibration signals in conjunction withvisible image features.) From this affine distortion information, the 6Dlocation of the card (x, y, z, tip, tilt, rotation) relative to thewebcam can be determined.

In processing the frames of image data captured by webcam 32, computer34 thus starts by examining each frame for watermark information, andcharacterizing the position of the card depicted in such frame byreference to such information. With this position information, the anglefrom which the sensor views each a-pel in each frame can be determined.(Again, parallax correction may be appropriate.) Once each frame of carddata is associated with its respective viewing angles, the reflectivityof different a-pels can be assessed at different angles—using aprocedure like that detailed in conjunction with apparatus 20. That is,the intensities of reflected light sensed from a given a-pel—viewed fromdifferent perspectives—can be applied to a centroid algorithm toestimate the wobble and azimuth angles at which such a-pel reflectivityis maximized. Given that the geometry of measurement is significantlyless controlled than during the production process, the precisealgorithms for estimating wobble peaks and angles is inherently muchnoisier but nevertheless still quite valid.

The resulting “random track sample” of the 2D albedo map for the cardcan be sent over the network, and compared against the albedo mapsstored in database 17. Despite the many degradations to which the cardmay have been physically subjected since its manufacture andcharacterization, the set of albedo data acquired by reader station 30will correlate, and will correlate strongly, with only one set of albedodata in the database. The card to which it corresponds establishes itstrue identity. This approach represents the complete data version ofauthentication, essentially boiling down to sending the database allcaptured frames of data (or at least heavily compressed frames).Practical situations (and generally not-for-free bandwidthconsiderations on communication channels) point toward finding dataeconomies at the camera head which can on the one hand greatly reducethe data volume required to be sent to the database, while at the sametime maintaining the essential albedo content required for formalizeddistinguishability testing processes.

(The assessment of geometric orientation, and estimation of the 2Dalbedo map, can be performed by computer 34, but need not be. In otherarrangements, the raw image data collected by reader 30—or a derivativethereof—can be transmitted to remote computer 15 for such processing.)

Given the simplicity of the reader station 30, it is unlikely that the2D albedo data it collects will be as accurately, and as finely,resolved as that produced by apparatus 20. However, such levels ofaccuracy and resolution are not required.

For example, instead of characterizing the reflectivity of each a-pel'swobble and azimuth angles to two or three significant figures (e.g.,0-90 degrees and 0-360 degrees), as might be achieved by apparatus 20, arelatively coarser estimate may be made. For example, referring tovector 11 in FIG. 2, the reading station computer 34 (or computer 15)may simply quantify the vector as leaning into one of four quadrants: I,II, III or IV (northeast, northwest, southwest, or southeast). In thisarrangement, each a-pel is associated with just a two-bit datum. Thisabbreviated data set can likewise be sent to database 17 for comparisonagainst the earlier-acquired measurements, e.g., by a Bayesian engine21. Again, only one previously-characterized card will highly correlatewith such data.

There is nothing magic about quadrants. The reflectivity may berepresented as a single bit (e.g., leans north or south; or leans eastor west). Or it may be represented with higher precision (e.g., fallinginto one of eight 45 degree swaths). Etc.

(Typically, the 2D albedo map acquired by apparatus 20, and stored indatabase 17, will be two- to ten-times higher in resolution than thealbedo map data collected at the reader station 30. To perform thecorrelation, the finer a-pel data in database 17 can be combined—acrossseveral small a-pels—to yield a vector sum corresponding to a largera-pel, of the sort estimated by reader 30.)

The reader station may provide audio or visual feedback to the user, toconfirm that the user's wave of the card was satisfactory. If the carddidn't move enough, e.g., if it didn't provide image viewpointsdiffering by at least a threshold amount (e.g., 5 degrees, 10 degrees,or 20 degrees), feedback might not be provided. If the card was moved soquickly that too few frames were captured (or the frames were tooblurry), feedback might not be provided. Likewise if the card movementwas outside the sensor's field of view too much. If no fault is notedwith the image capture, feedback indicating a proper wave can beprovided.

The data returned by the Bayesian engine 21 can take different forms. Itcould simply give a “green light” indication to the reader station,indicating that the card matched one in database 17. (Since the 2Dalbedo profile is so unique, details of the match may not be necessary;there is essentially only one possibility—the card is the one itpurports (e.g., by its text or watermark or barcode) to be.) In otherarrangements, the remote computer 15 can return to the reader station 30information about the card, or its bearer, obtained from database 17 (orother database).

In a particular arrangement, the watermark conveyed by the card is usednot just for geometrical orientation purposes, but is also decoded byreader station 30 to provide an initial assessment of the card's ID.That is, it may convey the name of the user, or their driver licensenumber. This decoded information may be sent to the database 17 with thealbedo data. In this case, the database's task is simplified. Itidentifies the card in its storage issued to that user, or with thatdriver license number. Then a simple comparison is performed between thereference albedo map stored for that card, with the albedo map estimateprovided by reader 30. If they correlate, the card is valid. (Othermachine readable data may be used for similar purpose, e.g., bar code,RFID, etc.)

(The watermark may be read from an aggregate image, produced bycombining several of the sampled images, after correcting each foraffine distortion. Technology for combining low resolution images byreference to encoded digital watermark signals, so as to yield a higherquality image, is taught, e.g., in published U.S. patent application20030002707.)

The ‘wave’ of the card in front of the webcam may result in the captureof 10-30 images, depending on the speed of movement. Generally speaking,the more images, the better. In some arrangements, however, it may bedesirable to limit the number of images processed, e.g., to a maximum of12. In deciding what images to keep, a variety of criteria may beemployed.

For example, if two images present essentially the same perspective ofthe card, then one may be discarded, or at least optimally averaged intothe other taking account of slight affine transformation changes.Similarly, if any image suffers a technical defect—such as glare orundue blur, it may be discarded too. (Image sharpness may be estimatedby transforming part or all of a captured frame of image data into thefrequency domain, and determining the amount of high frequency energy).Images that present the card at a too-oblique angle (e.g., more than 30or 45 degrees) may also be discarded.

In an alternative reading arrangement, the card is laid (or held)stationary, and a camera is waved over it. The camera in sucharrangement may be a cell phone. In this arrangement (as in others), theraw captured image data can be transmitted to a separate (e.g., remote)computer device for processing, or it can be processed by the samedevice used in the capturing of the data.

FIG. 12A details one “swoop” pass of a sensor over a card (or a card infront of a sensor). Each ‘x’ represents the orientation of the cardrelative to the sensor at a sample instant. The illustrated plot isshown in the tip/tilt frame of reference (with 0/0 indicating that thecard's z-axis is passing through the sensor lens).

At the first sample instant 41, the card is oriented with a tilt ofabout 12 degrees, and a tip of about 29 degrees, relative to the sensor.Subsequent samples are taken at different orientations. At eachorientation, the brightness of the a-pels are sensed.

The star FIG. 43 in FIG. 12A shows the tip/tilt at which thereflectivity from a particular a-pel 12 a is maximized. At all otherpoints on the graph, the brightness reflected from this a-pel is lessthan the brightness that would be sensed at position 43. By sampling theintensity of the 2D albedo profile at all the ‘x’ points, however, thecentroid algorithm allows estimation of the location of maxima 43.

It may be noted that the sample points in FIG. 12A define a two-part‘swoop’—the first going from sample 41 to 45, and the second going fromsample 45 back up to 47. The samples near 45 are relatively closelyspaced, indicating that the sensor (or card) movement is slowing. Thefact that the swoop generally reverses direction indicates that thesensor (or card) movement similarly generally reverses its movement forthe second part of the swoop.

(A two-part, generally-reversing, swoop isn't necessary; a one-way,unitary swoop can also be used. However, the former is preferred. Aunitary swoop generally characterizes the shape of the 2D albedo profilealong just one of its dimensions. The second part of agenerally-reversing swoop (provided it isn't identical to the firstpart) provides samples spaced in another dimension of the albedoprofile—allowing the profile to be characterized more accurately.)

Note that all of the samples in FIG. 12A are on the same side of maxima43. This will be the typical case. (Also typical is that the movementwill usually not provide a sample directly at the maxima point 43 fora-pel 12 a.) Preferable—although not necessary—is for the second part ofthe ‘swoop’ movement to take samples on the opposite side of the maxima.Such a sampling arrangement in shown in FIG. 12B. By sampling the 2Dalbedo profile on two sides of its maxima, the shape of the profile—andthus the location of the maxima—can more accurately be determined.

Although the calibration signals steganographically encoded with thepreferred digital watermark are highly useful in determining thegeometry of card-presentation-to-webcam, this geometry can be estimatedby other arrangements. For example, visible features can be identifiedon the imaged card (e.g., by pattern matching algorithms), and thedistortion of such features from their known shapes/dimensions can beused to infer card position. Likewise, if the outer rectangulardimensions of the card are known (e.g., 2″ and 3.5″), edge-findingalgorithms can be employed to identify the card edges, and thesefeatures can again be used to estimate card orientation relative to thewebcam. (Such arrangements are shown, e.g., in U.S. Pat. No. 6,959,098.)

Likewise, although the foregoing description did not make use ofwatermark data by apparatus 20 to precisely characterize position of thecard, such information is generally helpful and desirably would be used.

Different a-pels—even adjoining a-pels—on the card may have entirelydifferent reflectance curves. Such differences can be induced by themanufacturing arrangement. In an extreme case, the card can be hit witha meat tenderizing mallet—imparting a marked surface texture to thecard. Other arrangements for making the reflectivity curves relativelymore chaotic can of course be used.

Reflectance characteristics can also be tailored by choice of materials.Some materials will generally exhibit relatively diffuse reflectancecharacteristics (e.g., floodlight-like 2D albedo profiles). While suchmaterials can be used, it is preferable to identify materials that tendto have less-diffuse reflectance attributes, so that the maxima fromeach a-pel can more readily be defined.

In alternative arrangements, each of the oblique card images captured byapparatus 20 and reading station 30 can be normalized to their originalrectilinear shape and their original scale, prior to estimation of the2D albedo map. Again, this can be done by reference to the watermarkcalibration information embedded in the card.

A refinement may be made to the watermark-based image registrationprocesses described in the cited patent documents, above. Normally,these processes produce an estimate of parameters that characterize theaffine distortion of an image. The image is then processed tocounter-act such estimated distortion, and the watermark payload is thenread.

This may be refined as follows: instead of using just the originalestimate of the distorting parameters, try perturbing these estimatesslightly. For each perturbed set of estimates, counter-distort the imageaccordingly, and sense the strength of the watermark payload signal. Itmay be found that counter-distorting with one of theseslightly-perturbed distortion estimates yields a stronger watermarkpayload signal than occurs using the original distortion estimate. Insuch case, the perturbed estimate more accurately characterizes thedistortion.

By use of such refinement, still more precise determination of cardposition/orientation may be achieved (e.g., angular resolution on theorder of a sixtieth of a degree may be obtained).

The Bayesian engine 21, at one level, simply checks the albedo dataprovided from reader station 30 with albedo data corresponding to one ormore cards earlier characterized by apparatus 20 and stored in database17. One check, as noted, is correlation. This can comprise, e.g.,computing a dot product between two albedo maps represented in azero-mean version. (E.g., each set of albedo data can represent leaningof the maximum reflectance vector in the east/west dimension (tilt) as−1 for west, and 1 for east. Likewise −1 for north and 1 for south. Ifthere is no correlation between the albedos, the sum of such productswill tend towards zero. If there is correlation, the prevalence ofsame-sign products will cause the sum to increase. This correlation willbe apparent even if 95%-98% of the a-pel reflectivity characteristicsare changed, e.g., by wear, during the card's service life. Thosechanges will generally be random; correlation of the remaining 2%-5%will establish the genuineness of the card.)

The albedo data sensed for a particular a-pel might also be processed inconnection with a “confidence” factor, e.g., ranging from 1 to 5(maximum confidence). In the example given above, in which the sensedalbedo “lean” from each pel is quantized as being in one of fourquadrants (I-IV), the confidence factor can be less if the lean isslight, and more if the lean is great. (More sophisticated confidencemetrics can of course be employed.)

Table 1 shows the respective quadrant into which each of plural a-pels“leans”:

TABLE 1 I III III II IV IV I I II III I III II IV IV I II IV I II III IIV I I

Table 2 shows the corresponding “confidence factors” for each a-pel:

TABLE 2 2 3 2 4 5 2 3 3 1 2 4 5 1 2 3 4 2 4 3 3 5 2 3 4 1

These confidence factors can be used to bias the weight given each ofthe respective a-pel data, in identifying a reference card with theclosest match. Perhaps the simplest biasing function is simply todiscard all of the a-pel data that does not have a confidence of ‘V.’Such a filtered set of a-pel data is shown in Table 3:

TABLE 3 IV III III

Thresholds other than ‘V’ can, of course, be used.

In slightly more sophisticated arrangements, a-pel data for all pelshaving confidence of II or more are used, and the matching algorithmweights the degree of a match in correspondence with the confidencefactors of the a-pels used in the analysis.

The Bayesian engine can consider further factors. For example, it may,over time, learn that certain individuals present their card along a“swoop” path that seems to have certain consistencies. Detection of apath following this expected pattern can tend to affirm that the card isbeing used by its authorized owner. Marked difference from such anexpected swoop pattern may prompt the reader to have the user repeat thecard presentation, or otherwise focus further inquiry on the user.

In some arrangements, the operation at the database involves retrievingthe albedo data previously stored for a particular card, and comparingit with data sensed from a reader device—to ensure they correspond in anexpected manner. If so, the card is confirmed to be the same physicalcard from which the albedo data was originally measured. This sequenceof operation is used, e.g., when a tentative identification of the cardcan be made, e.g., by reference to a name or license number printed onthe card, or encoded thereon in machine-readable form. This tentativeidentification is then used to identify one particular set of albedodata in the database for comparison.

A more complicated situation arises when no tentative identification ofthe card is made before consulting the database. In this case, the taskis to identify a “best match” between the albedo data derived from datasensed at the reader device, and sets of albedo data earlier stored inthe database.

Art known from other disciplines can be applied in this undertaking,such as “robust hashing” art known in audio/video fingerprinting andelsewhere, and associated database search optimization techniques. Forexample, it is not necessary to check the new set of sensed albedo dataagainst all of the old albedo; certain old data can be almostimmediately excluded from consideration (e.g., by techniques such asdatabase pruning). The albedo data can be distilled into a smallerrepresentation, which is robust against many corruption mechanisms. Suchtechniques, and other useful technologies, are detailed in WO02/065782,US20060075237, US20050259819, and US20050141707.

Different albedo maps can also be characterized for different spectrumsand/or polarizations of illumination.

The assignee has run tests, using a robot-controlled test jig, at twodiscrete angles of tilt in the y direction, covering −10 to 10 degreesat one degree increments in the x-direction. Plural seemingly-identicaldemonstration driver licenses of two different designs have beenemployed. One license design is particularly interesting because it islaminated with the 3M Confirm laminate, which is comprised of littlebeads, which serve as wobble randomizers.

The per pixel luminance measurements show consistency between imagescaptured at a given tilt angle and position on the robot mount. Also,the luminance measurements vary with tilt angle and position on the card(pixel number). When a new set of captures are taken of a different butvisually identical card, the per pixel luminance measurements at aspecific tilt angle differ from those of the first card.

In the arrangements detailed above, the albedo function is generallystatic. However, it is possible for the object's albedo function to bechanged (either at the time of initial manufacture, or subsequently).

The simplest arrangements allow for the albedo data to be changed once.Various chemical formations (e.g., photographic emulsions, photoreactiveinks, etc.) change state in response to particular stimuli(illumination, chemical, thermal, etc.) If a card is provided with suchmaterials (e.g., on the surface, or embedded within), stimulating samecan induce a change that affects the albedo function.

One particular arrangement employs a card having photoreactive ink,illuminated with a laser via a micromirror array (perhaps up to 10-50million mirrors). By controlling the micromirror orientations, regionsof the card are illuminated, or not, by the laser light. Correspondingchanges are thus induced. (The micromirrors can be controlled so thatlaser light exposes some regions for different time periods thanothers—further tailoring the change to the albedo function.)

Another arrangement employs a chemical composition that reacts to laserillumination at a particular wavelength by producing a broad albedo peakin the direction from which the illumination is applied. Desirably,illumination at a different wavelength removes this effect, e.g.,restoring the surface to a quasi-“virgin” state, or causing a randomalbedo response, or a peak in a different direction.

Yet another arrangement employs a material that changes its opticalindex of refraction following exposure to a given chemical compound,such as water or a solvent. Such a material—spanning the cardsurface—may be spritzed with liquid (e.g., with a mist or aerosol spray)to change its optical properties. Some such materials are described,e.g., in Kim, Singh and Lyon, “Label-Free Biosensing with HydrogelMicrolenses,” Angewandte Chemie International Edition, Volume 45, Issue9, Pages 1446-1449,2006.

In each of these arrangements, although only a single state change isusually possible, several successive generations of data can be inducedby applying the changing mechanism sparingly—changing only a subset ofthe a-pels (often a random subset) each time. For example, the liquidspritzing in the foregoing example may be light enough to alter just 10%of the a-pels. Even if performed 10 times, further changes may besubsequently achieved since—statistically—an action that leaves 90% ofthe a-pels unchanged, if repeated 10 times, still leaves about 35% ofthe pels unchanged. The other change-mechanisms can likewise be appliedto a subset of card features.

Such techniques can be incorporated in the work flow of a card issuancesystem, processing cards either before or after variable data (e.g.,name, photo) are applied.

Other arrangements allow the albedo function to be changed virtuallywithout limit. Consider, for example, a card that has a texturedlaminate, comprising micro-droplets of clear thermoplastic that isessentially rigid at temperatures up to about 150 F-250 F, but thatbecomes pliable above such temperatures. Such droplets may originally beuniform in shape (e.g., hemispherical). However, such a card can beheated to the point the droplets become pliable, and a randomly texturedmedium (e.g., plate, roller-wheel, etc.) can then be impressed into thelaminate surface, causing the droplets to deform in random ways. Whencooled, the card will have a radically different albedo profile thanformerly. The process can be repeated as many times as desired. (Alaminate without micro-droplets, but simply comprising a layer ofgenerally flat thermoplastic material, can similarly be employed.)

Instead of impressing the laminate with a physical texturing medium, thelaminate may be spot-heated, e.g., using a raster-scanned CO2laser—pulsed in a random (or a controlled) manner. Temperaturedifferentials induced by such technique can cause the plastic materialto deform.

In one particular arrangement, a pulsed laser obliquely illuminates alaminate having microdroplets, as shown in FIG. 13. By illuminating thedroplets from different directions, different deformities can beinduced. This can be effected by using plural lasers, or with a singlelaser and a mirror arrangement (e.g., an electronically-steerablemicromirror array). Or by use of a single laser, and moving the card,etc.

Instead of illuminating the plastic material from different directionsto yield differently-shaped distortions, the plastic may be illuminatedfrom the same direction, but for different periods of time. Other sucharrangements will be evident to the artisan.

Still another arrangement bonds a micromirror array/microlens layer ontoa card substrate. (The lenses can be movable with the mirrors, orfixed.) Instead of being electronically steered, the micromirrors canrest on microdroplets of deformable plastic, and point in a directiondependent on the shape of the respective underlying microdroplet. Themirrors can be relative transparent at infrared, allowing emission froma CO2 laser to heat the droplets of deformable plastic through themirror elements. By heating the microdroplets from different directions,and/or for different times, the directions in which the mirrors pointscan be varied and controlled. Such a material can be “written” from oneangle, and “erased” from another (and read straight-on).

Yet another arrangement places a CCD lens array atop a photo resistlayer, on a card. The card can be read from one angle, and written fromanother (and read straight-on).

A point-of-sale terminal can illumine the card at the angle necessary toread the data.

In still other arrangements, a card may be re-shaped withoutarrangements as elaborate as detailed above. A card may simply be passedthrough a feeding mechanism that impresses a shaped roller against itsface. (A simple arrangement is a sand paper-roller.) Even withoutelevating the temperature of the card, its albedo function may bealtered.

Still other arrangements employ intaglio techniques (either inked, orinkless) to shape the surface of a medium in a desired fashion. Suchtechniques are known to the artisan from references such as Deinhammer,“The Implication of Direct Laser Engraved Intaglio Plates on BanknoteSecurity,” SPIE Vol. 6075, February, 2006, as well as US patentdocuments U.S. Pat. Nos. 6,840,721, 20030145747, 20040025728,20040232108, 20050072326, 20050115425, 20050139100, 20050193909, and20060151989, and international patent publications WO05/002869 andWO06/045128.

The foregoing and other techniques allow shapes including Morsetopologies to be formed on an object. Morse surfaces can be used totailor directional albedo in arbitrary fashions (e.g., by changing theelevation of topological peaks, changing the position of saddle points,changing the depths of local depressions, etc.). (C.f. Milnor, “MorseTheory,” Princeton University Press, 1963, ISBN 0-691-08008-9; andZomorodian, “Topology for Computing,” Cambridge Monographs on Appliedand Computational Mathematics, 2005.)

Metameric inks, whose response decays or changes over time, can beemployed to introduce a temporal variability to the wobble response.Thermics provide another dimension, varying the outputted response inresponse to temperature. Different directional albedo signals may thusbe sensed in different domains, e.g., luminance, red, green, blue,metameric, etc.

By such technologies, data densities on the order of up to 10,000Morse-els per square inch may be achieved (homage to Morse). Thedirectional albedo (luminance) of each element can represent on theorder of 2-8 bits per data from angle alone. The other dimensions ofdata provide still more bandwidth.

In still other arrangements, the albedo function of a surface is variednot by application of physical or thermal stimulus, but rather byelectrical or molecular changes that serve to vary local reflection.

Altering the albedo function of a card can be done each time the card isinvolved in a transaction, or only at certain times. A point-of-saletransaction terminal can include components for reading the albedofunction and for changing the albedo function, so that aread-modify-reread sequence of operations can be performed. (The datacollected in the ‘reread’ operation can be stored locally or centrallyfor reference, e.g., used in a subsequent read operation to verify thecard.)

The albedo function can also be a function of the ink used to print thecard. For example, pearlescent or metameric inks can be used. Magneticinks can also be used to impose some directionality (which may berandom) on the illumination reflectance profile.

More advanced materials can also be employed, such as “quantum dots”(semiconductor nanocrystals). Quantum dots are available commerciallyfrom vendors including Evident Technologies (Troy, N.Y.), UT Dots, Inc.(Savoy, Ill.), and American Dye Source, Inc. (Quebec, Canada). They canbe incorporated, e.g., in bead or dust form, into inks, plastics, andcoatings used on licenses. These materials exhibit a narrow andcustomized emission spectrum, with an emission amplitude that isdependent on excitation wavelength. Such materials have knownapplications in anti-counterfeiting. As explained at the EvidentTechnologies web site:

-   -   Two critical aspects of quantum dots give them the ability to        act as an encrypting device for anti-counterfeiting: their        narrow and specifiable emission peaks, and their excitation        wavelength dependent emission intensity. With these traits,        several different sizes (and therefore emission wavelengths) of        dots can be combined with several different wavelengths of        excitation light in order to create an almost infinite variety        of emission spectra. Each of these spectra correspond to one        coding combination, which can be made as arbitrarily complicated        to duplicate as the encoder wishes. This process works as        follows.    -   Each quantum dot size corresponds to a given emission peak. If        dots with different emission peaks are mixed together in known        quantities, the resulting emission spectrum contains each        emission peak present at some measurable intensity. This        intensity will be dependent on both the quantity of dots present        and the excitation intensity (or intensities, if several sources        are used). By fabricating materials containing predetermined        amounts of quantum dots which emit at arbitrary wavelengths, and        then establishing their emission spectra at arbitrary excitation        wavelengths, one can create a “code” based on the relative        intensities of emission peaks. For example, if one combines        equal amounts of 1000 nm, 1500 nm, and 2000 nm emission dots,        and excites them at 800 nm; it would yield a different spectral        code than unequal amounts of 1100 nm, 1600 nm, and 2100 nm        emission dots excited at 900 nm. By changing the number of dots,        their individual concentrations, their emission peaks, or their        excitation wavelength, one can create and record a nearly        unlimited variety of different spectral codes which can be        easily inserted into plastic sheaths, inks, dyes, fabric, or        paper, allowing quantum dot anti-counterfeiting encryption to go        anywhere.

In a point of sale terminal that illuminates—with a particularillumination spectrum—a card having quantum-dots, the resulting emissionpeaks can be detected by the terminal and employed as a form ofmachine-readable data—just like bar codes, RFIDs, digital watermarks,etc. The data thus represented can be employed in the variousapplications known for such other machine-readable data, including usein conjunction with other machine-readable data conveyed by the card, incryptographic key applications, as a fingerprint, etc.

One particular arrangement employs several layers of quantum dots, eachlayer having different characteristics (e.g., emission spectra). Thelayers are separated by (or include) photoreactive layers that can bemade successively transparent by appropriate stimulus.

From the top layer of quantum dots, a first characteristic spectra isemitted (a simple example may be pure red light) in response to aparticular illumination. If the photoreactive material beneath (oraround) the first layer of quantum dots is made clear, the quantum dotillumination also extends down to the buried, second layer. Itsdifferent emission spectra (e.g., blue light) changes the net spectrasensed from the card. Likewise, if the photoreactive material beneath(or included in) the second layer of dots is made clear, the quantum dotillumination extends down to the buried, third layer. Its emissionspectra (e.g., yellow light) combines with that of the other layers toresult in a third, unique, net emission spectra. The varying emissionspectra can be sensed from the card (e.g., in a simple arrangement, as8-bit data from red-/green-/blue-filtered CCD elements), and theresulting data can serve as a changeable (renewable) key, withwell-known cryptographic benefits.

A similar arrangement can include two layers of quantum dots, separatedby an intervening layer that is originally transparent, but which can bemade relatively opaque by application of stimulus (e.g., laser energy ina certain band) thereto. (Or, the photosensitive material can form partof the layer in which the dots are included, instead of comprising aseparate layer.)

By arrangements such as the foregoing (which may be combined), thewobble function of an object may be tailored as desired. Thus, insteadof an uncontrollably random function, a controlled (and optionallypseudo-random) function may be achieved.

Exercising control over the wobble function allows knowninformation-theoretic principles to be applied, enabling the wobblefunction to represent a desired payload that can be reliably detecteddespite physical corruption of the object and distortion of individualwobbles.

One such principle is use of error correcting codes, such as turbocoding, BCH coding, Reed-Solomon block codes, convolutional codes, etc.Such techniques rely, e.g., on oversampling, i.e., representing N bitsof payload data as M bits of signal, where M>N. The redundancy inherentin such arrangements allows errors to be noted and corrected. Suchtechniques can also employ likelihood measures—indicating the relativeprobability that a given bit has a given value (akin to the confidencefactor tables presented above).

Another principle that can be brought to bear is predictive filtering.Such techniques are taught, e.g., in U.S. Pat. Nos. 7,076,082 and6,614,914. In one particular embodiment, a 3×3 region of a-pels isconsidered. In normal media, the wobble of the center a-pel may normallybe expected to be correlated to the wobbles of the 8 surrounding a-pels.If the vector average of these surrounding a-pels is calculated, theresult can be used as a baseline against which the wobble of the centera-pel can be judged for variance from this natural mean. By suchtechnique, signals corresponding to the deliberately-induced wobblefeatures can be raised out of the “noise” of the (typically lowerfrequency) wobble characteristic that may naturally occur in a medium.

Using the cited techniques, a card having 50,000 virtual a-pels arrayedacross its surface may reliably convey a key code comprising, e.g.,500-5000 bits or more. Such key codes can be used in myriad knownmanners, some of which are detailed in the references cited at thebeginning of this specification.

One particular application of wobbles is in challenge/response systems.The goal of such systems is to render useless any knowledge that anattacker may glean through interception of communications betweenparties. This is traditionally accomplished with one-time passwords. Oneof approach (of many) to the construction and use of one-way passwordsis to use a challenge and response system. Traditionally, threecomponents are used on the client side of such systems: a base secret, arandom challenge, and a hash/encryption function (or othermathematically one-way function).

A challenge is issued by the authenticating party. The client combinesthe challenge with the base secret and runs the result through a one-wayfunction. The resulting output is transmitted (e.g., back to theauthenticating party) for validation. The recipient of the outputperforms the same calculation, and compares the calculated and receivedresults. Through such use of the one-way function, the base secret isnever transmitted in the clear between the parties.

Employing wobbles, the physical card (or other object) can serve as thebase secret and/or the one-way function. The random challenge canconsist of an instruction to image the card under conditions of specificillumination, position, etc. A sample authentication scenario mayproceed as follows:

-   -   1. Server issues a challenge to the client (rotation of token .        . . say 45 degrees);    -   2. Client communicates the challenge to the end user (“Hold card        at approximately 45 degrees”); user images the rotated card;    -   3. Client reads a watermark from the card to determine card's        rotational alignment, and senses wobble signals; resulting        wobble data is sent to the server;    -   4. The server, based on wobble measurements earlier taken from        the card, determines the wobbles that should be sensed from a        card at the specified rotation;    -   5. The server compares the results received from the client,        versus those it calculated; if they correlate as expected, the        client is authenticated.        It will be recognized that if the wobble data sent from the        client is of a coarse “quadrant” variety (e.g., as explained in        connection with the tables above, wherein the lean of the wobble        is identified within one of four quadrants), then rotating the        card even a fraction of a degree causes certain of the wobble        vectors to progress into the next quadrant—but not others. The        server—with its more accurate quantification of the wobble        directions—can accurately model which wobbles will transition        into each quadrant, for any given rotation. But interception of        one coarse wobble signal does not allow an attacker to predict        the signal when the card is slightly rotated. (Of course,        rotating 90 degrees should cause each wobble to progress into        the next quadrant.)

The just-detailed arrangement requires issuance of a specific challengeto the user, and requires the user to hold the card in an appropriatefashion. The “S/Key” challenge and response protocol (sometimes known asLamport's scheme, and commonly used as a onetime password system)eliminates this communication, and instead operates on succeeding hashesto be created from a common base secret. As one work has explained:

-   -   The [S/Key] technique uses a sequence of hashes, each computed        from the previous one in the sequence. The server stores the        last hash in the sequence. To log on, the client provides the        next-to-last hash in the sequence as a one-time password. The        server takes the client's one-time password, hashes it, and        compares it to the stored hash. Both should match. Then the        server replaces the hash in the client's password entry with the        password just provided.        In the case of wobbles, before a card (or other token object) is        issued to the user, it is configured to encode a large number of        temporary passwords, all calculated off the base secret. (Once        the passwords are used up, the card can be disposed of.) Each        unique signature calculated from the wobbles is another one-time        use password calculated on the base secret (the construction of        the card).

At first blush, there may seem to be no significant difference betweenthe two techniques, as a challenge in the first is equivalent, in thesecond, to needing to know which password in the sequence needs to besubmitted to the server for authentication.

By loosening the definition from “password in sequence” to “an unusedpassword,” then the instructions (“challenge”) to the end user becomesthe much simpler “wave the card in front of the camera” set.

Thus, in the simplest embodiment, the client would pass either all theobserved frames, or calculated wobble vectors, to the server.

An optimization to this is, at the time of session initiation with theserver, the server transmits all positions (based on the watermark) thathave been used. This allows the client to provide better feedback to theuser during the validation step.

In embodiments in which a cell phone device (which term is used toencompass devices such as PDAs, iPhones, Blackberries, etc., whethercommunicating over a cell network, or WiFi, or WiMax, or Bluetooth, orotherwise) is used as an optical sensor, the wobble data therebyacquired can be used in conjunction with other operations performed bythe device. For example, it can authenticate the cell phone to conduct aparticular transaction, serve to enter a password to gain access to aprotected network domain, authorize use of a user's credit card data,etc.

FURTHER DISCLOSURE

A sample embodiment makes use of the 2-Pi-steradian albedo—to use the‘proper’ science phrase—better known as the directional reflectanceprofile—for each and every resolution element or local group ofresolution elements on a card. At a reading station, a card is moved infront of a sensor, presenting the card from different angles, as opposedto being flatly scanned on a scanner.

Each square millimeter of the card, for example, has its commonlyunderstood “grey value,” “density,” “reflectance,” etc. This commonunderstanding is an approximation to the (spectral)-directional-albedoprofile. Sophisticated models often distinguish between objects whichare illuminated in a diffuse “from all directions” type of lightingsource, and the more special case where an object is being illuminatedfrom a specific angle or otherwise selectively as a function of angle.The latter case thus has two forms of directionality: source directionand reflective direction. The resultant “albedo map” is thus a functionof 4 dimensions: the reflectance of a unit of light energy transmittedfrom a given 2D direction and detected at a separate 2D direction.

The distinction between coherent (e.g., laser) versus incoherentillumination may be included for special situations, but the case ofcoherent light brings with it “interference” which modulates thesedirectional albedo functions at very fine directional scales. In thepresent discussion, coherent light illumination isn't considered(although it can certainly be used in various embodiments). Instead, theexemplary arrangement focuses on low end cameras in effectively diffuseillumination situations.

Another special case in all of this is 3M's retroreflective technology,which viewed in the above 4D description is the 4D albedo map where thereflectance is ‘1’ for all 4D points where the first two coordinates areidentical to the second two coordinates, and ‘0’ everywhere else. Noreal document or physical system approaches this ideal.

In a forensic setting, where lighting can be controlled as to affect all2-Pi steradian angles of illumination on an object, and likewise asuitably distant (say 2 meters away) high-quality camera can separatelytake images of the illuminated object from all 2-Pi steradian angles(independently), an empirical set-up is thus established that can samplethe 4D albedo map for any given object. Practically, one would need tomove a light source to successive given directions relative to theobject, where at each illumination direction the camera is moved throughall of its sampling directions. A mere 32 illumination directionsmatched to 32 detection directions gives 1024 high resolution images tobe taken for what amounts to be a fairly coarse sampling of the full 4Dalbedo map.

For most low end camera applications, we can greatly simplify ourforensic lab and the subsequent discussion by either accepting generallydiffuse lighting as the standard illumination mode, or perhaps boil downillumination to six categories: generally diffuse and five semi-diffusefrom straight-on, up, down, left and right. The six-mode approach shouldbe adequate for almost all general-low-end-camera applications—possiblyeven a bit of overkill.

So, proposition number one is that in a forensic lab with a good 12-bitgrayscale camera sampling at, say, 128 different directions on any givensingle illumination condition, identically produced cards willnevertheless give rise to quite distinguishable albedo maps simply dueto manufacturing processes involved with the stock, printing, laminates,etc. If this is not the case, it should not negate the overall approachdescribed here, but it will possibly make it more challenging as anengineering matter. Be this as it may, albedo map “variationaldifferences” on the order of at least a few percent if not 5 to 10percent should be expected and readily detectable. “Variational” refersto wobbles as a function of read-angle, and is deliberately an informaland secondary term, where the main point is that the maps aresufficiently different.

Assuming the forensic lab albedo map differences are confirmed across awide range of examples, this leads to the first test for garden varietycameras: by waving two identically-produced “regular-old” cards in frontof a camera in a controlled, reproducible way, ensuring at least a 20degree read-angle swath, will one card consistently produce a data setwhich is distinguishable from the other, where for example 15 frames ofimage data are collected? The answer is expected to be ‘yes,’ but itwould not be surprising if the difference was so slight that onlycarefully controlled conditions applied multiple times would benecessary to meet basic distinguishability statistics. The plausibilityargument that there will be meaningful signal gets down to the fact thatsome ten or twenty thousand effective locations on the card would besampled 15 times each, producing a lot of data for one binary decision:same or different. This baseline scenario ultimately boils down tostraightforward Bayesian decision statistical descriptions.

How might such an arrangement be hedged? A first line of hedge is tosearch for manufacturing methods which enhance the resulting Bayesianstatistics, period. Things as simple as loosening the tolerances onlaminate thicknesses is but one simple and potentially powerfulexperiment. Other loosened tolerances, and introduction of randomfunctions, could similarly be used, alone or in combination—many at lowor no cost (or effecting a cost savings). Skipping ahead, one would hopethat two or three key methods could start to make the Bayesian “swipesignatures” (if you will) substantially and reliably different from eachother.

Next up is the hedge-of-hedges, represented in the extreme by suchthings as the 3M retroreflective materials. The key concepts here are“by-design” and some position on the “no-cost to costly scale.” Thegeneral game here is to continue to enhance the Bayesian properties,while now beginning to pay more attention to angular wobble propertiesand how they relate to such loose specifications as “minimum 20 degreeangular presentation of the card.” Also, alluding to how camera dataneeds to be captured, compressed and shipped to some trusted decisionunit, these practical considerations have to be taken into account asby-design albedo-map properties are created and tested (and obviouslytaking into account cost in all its various forms).

This immediately preceding discussion presumed the “two identical cardspresented to a camera in a reproducible, controlled manner.” This isobviously not how cards will be used, but it was important to establishthe baseline differences between otherwise identically produced cards.

So now we move to normal usage. Presumption number two is that kids tograndmothers can easily be taught (virtually entirely by tactileexperience) to present cards to cameras within some technically definedspecification on distance, angular movement, speed, number of capturedframes, etc. User testing should be able to establish “99% behavioralbounds” which then become the hard targets that engineers treat asdesign gospel and Bayesian constraints. Normal usage will include thesix modes of lighting conditions, the specs of any given camera, thenumbers of frames acquired and the above-defined limits of behavioralbounds.

A digital watermark, e.g., as detailed in U.S. Pat. Nos. 6,614,914 and6,947,571, will provide the informational basis for precise 6dimensional measurement of the movement of a card in front of thecamera: X, Y, Z, pitch, yaw, roll. The basis is thus formed to uniquelydetermine how our ten to twenty thousand albedo-beacons travel throughspace and which read-angle is being presented to any given frame. Wehave our guide to map any given movement back into a card's uniquealbedo map, forming a comparison between a live event and a re-enactedtrace through a stored, trusted map.

At all but an extreme theoretical level, we're at a pretty good pointright here. All grandma may be doing is sending instance after instanceof these ˜20K by 15 albedo swaths back to a trusted decisionmaker foradjudication. The very low-end nature of the camera will ensure thatthese essentially randomly-complicated and very subtle signatures arequite buried in various noise and distortion soups, a first hint atwhat's good for the decisionmaker (because we've already designed inplenty of signal in the cacophony of noise) and problematic for thewould-be counterfeiter. The allusion to “random” refers to the idea thatthe wobbles will be fairly “random about the Lambertian-profileexpectation” in and around the straight-on to 20/30/40 degrees off-angledirections. The Lambertian-profile is the one you would expect onaverage from a normally reflective surface. The general notion at thispoint is that this card can be presented thousands of times, each timeproducing essentially new data blobs.

So we next consider the attacker with a well-equipped lab.

Will such an attacker be able to discover and record the unique albedomap of a given card, given the possession of the physical card? Ofcourse . . . they can rig up a comparable forensic lab set-up. Thepractical issue gets down to how long does someone need access to a cardin order to gather sufficient forensic data. Certainly longer than thecard-swiping-in-the-pocket waiter at the fancy restaurant; but ahalf-minute in the process outlined above, which characterizes the cardat the DMV at the time of its issuance, will do.

Will a data-tapper be able to tap the unencrypted data blob feed fromhundreds of presentations of the card and slowly be able to recreate theunique albedo map of the card? Of course, assuming they also are tappingthe watermark-provided 6D swath vector as well, or use some other formof 6D registration in order to form a stable basis to start averagingthe albedo map. With enough presentations (along with reliable 6D data),the lower frequency albedo map data (wobbles) will begin to show up.

So, physical possession of the card, as well as tapping6D-enabled-hundreds-of-presentations-unencrypted-data-blobs will bothenable sleuthing of the card's albedo maps. Let's call this entity the“crude-sleuthed-map” or CSM.

The next question is, given this CSM knowledge, what can the rogue dowith it? Can they physically reproduce a card that sufficiently mimicsthe map so as to fall into the industry standard Bayesian decisionstatistics (which would be a published standard by a decisionmaker ordecisionmaker classes)?

Data-wise, they will clearly be able to simulate a low-end camera,impress the CSM onto that data, package it up and ship it to thedecisionmaker as if they were grandma doing it. They couldpseudo-randomize presentation 6D swaths as well, new instances of cameranoise, even lay down a base layer of a “nearly identical” card datareplete with digital watermarking data, then overlaying the CSM layer.One can imagine a fair amount of sophistication in simulating thepresentation of a card to a camera, given the CSM. In any event, thisone needs to be clearly flagged as a usage-model dependent attack wellworth fully exploring in each and every situation, market, application,whatever.

Certainly there are other kinds of data-domain-only attacks that need tobe defined, elucidated, studied, counter-attacked and catalogued. Forinstance, where does threshold attacking of the Bayesian decisionmakerfit in, if at all? It is unclear if you will ever get a “yes” in thefirst place from a decisionmaker if one doesn't have the card or theCSM, or maybe you get a lucky “yes” every billion tries and this becomesthe seed of a threshold attack? Then there's the whole question of thesecurity of the decisionmaking methods, systems, networks, etc., whichall seem to fall into application/market specific cryptographicdefinition and cataloguing.

The question of physical reproduction given the CSM is a moreinteresting question. Here, the CSM is synonymous with having the card.

To start with, we've already established in our designing above that thesame relatively high end and sophisticated machine cranking out cardafter identical card has no chance of recreating the CSM, even givenknowledge of the CSM. (This latter statement is ultimately a function ofthe design methods we settle on and how “pro-active” they are versus“reactive,” but it is a safe presumption that the high end originationmachine will not be able to even come close to reproducing the card'salbedo map even given full knowledge of the CSM).

So that leaves the option of a specially designed machine that attemptsto not only duplicate the nominal identical design of the card, but thenimpress upon it an artificial duplication of the CSM in a way that doesnot include additional albedo map wobbles that will throw the reproducedcard's CSM out of the published Bayesian bounds.

First of all, building such a machine would be an extreme challenge atmany levels, with but one being that the published Bayesian bounds—thatthe machine ultimately has to answer to—do not need to be limited andcan evolve. Probably the biggest challenge would be proactivelysculpting the surface properties of a laminate or equivalent, or some80-90% of the 20K surface elements that is, to the required wobblypatterns of the stored CSM. Even if those wobbles are extremely lowfrequency and tame, which they generally won't be, it simply isdifficult to conceive of a machine which could do this. Mask-basedetching? Nano-machines? Microsurgery equipment?

And then there would be the residual albedo-signature noise to contendwith. The original registration of the albedo map of the original cardmight presumably also characterize the higher frequency statisticalattributes of the albedo map. The original stored CSM used by thedecisionmaker could capture this data and use various measures as a kindof a simple “check-sum” on a given read, forcing our miraculous machineto first understand these properties as part of the CSM dataset, andthen furthermore reproduce these statistics.

In any event, serious study and cataloguing of potential CSM-reproducingmachines is required. Presumption number three to this whole approach isthat this miraculous machine will, at the very least, be exceedinglyexpensive, and better yet essentially beyond the reach of current andnear-term technology.

So, attack-wise, given knowledge of the CSM, you've got the datawisesimulation of a camera presentation and you've got the miraculous but atthe very least quite expensive CSM-reproducing machine. Each requiresthe not so trivial step of gaining knowledge of the CSM.

Going back to the CSM-reproducing machine, at this point might it beequated to the mythical three-embedded-room-deep machine at the NSAwhich molecularly CATSCANS smart cards in order to sleuth their secrets?It would not be surprising if a proof is established that the technicalchallenges in creating a CSM-reproducing machine are on the sametall-order scale as creating the machines intended to bust smart cardsand other tamper-proof electronics.

CONCLUDING REMARKS

This specification covers a lot of ground—much of it new. The breadth ofapplication of the disclosed technologies is large, as will be apparentto artisans skilled in the field.

For example, it will be apparent to artisans that elements of thedisclosed arrangements can be employed in on-line purchasing of goodsand services, and on-line bill paying. Application of pseudo randomcryptographic keys—of the sort represented by, e.g., wobble data—to suchactivities are well understood. This is but one of many examples wherethe present specification enables novel applications.

It is expressly contemplated that the technologies, features andanalytical methods detailed in this specification can be incorporatedinto the methods/systems detailed in the earlier-referenced documents.Moreover, the technologies, features, and analytical methods detailed inthose documents can be incorporated into the methods/systems detailedherein. (It will be recognized that the brief synopses of such priordocuments provided above naturally do not reflect all of the featuresfound in such disclosures.)

It will be recognized that elements of the arrangements detailed hereincan be used advantageously in other contexts. For example, while adirectional albedo function has been employed in detailed arrangements,this function has advantageous utility elsewhere. Conversely,alternative implementations using technology detailed herein do not needto involve a directional albedo function.

More generally, it should be recognized that this specificationdiscloses a great number of arrangements and included sub-combinationsthat are useful and non-obvious apart from the larger embodimentsparticularly described. Thus, no particular element or act recitedherein is believed to be essential to definition of patentable subjectmatter. Methods and apparatuses in which detailed elements/acts areomitted, or substituted with other elements/acts, are expresslycontemplated. Thus, by way of example and not limitation, an identitycard is not essential (the detailed embodiments can be practiced, e.g.,to identify a particular physical object, such as a wristwatch); anoptical sensor is not essential (identification can be based ondifferent physical measurements, such as of acoustical properties); arandom track of an object before a sensor is not essential (a carefullycontrolled track may be employed), watermarked data is not essential(e.g., position—if relevant—can be determined by other means), etc.,etc.

Moreover, novelty does not reside only in the overall system, but alsoin subcombinations disclosed herein. For example, the measurementapparatus of FIG. 3 is believed patentable per se, as is the concept ofuniquely identifying an article by reference to its directional albedofunction, as well as imparting a deliberately random feature to alicense prior to issuance, so too perturbing watermark-estimatedorientation data to generate refined orientation data, and likewiseweighting wobble data in accordance with a confidence factor indetermining a match, etc., etc. (Some such subcombinations areparticularly noted in the listing that follows, although such listing isnot exhaustive.)

Applicants expressly note that results achieved by certain combinationsand subcombinations may be achieved by othercombinations/subcombinations that are straightforward to artisans in thefield—informed by the teaching of this specification. For example, whilethis specification teaches that a card may be imparted a random surfacetexture by hitting it with a meat tenderizing mallet, the artisan willimmediately recognize that such a result may be achieved by myriad otherstraightforward means (e.g., rubbing with sandpaper, laser etching,etc.)

Arrangements using concepts detailed herein can also make use ofmachine-readable technologies (e.g., bar codes, RFIDs, magnetic stripes,etc.), or can be substituted for such technologies in previously knownarrangements.

A few of the novel arrangements detailed herein include:

A. A method comprising: assessing directional albedo data relating to asurface of a first article; and storing data corresponding to saidassessment, for later verification of said article.

B. The method A in which said assessing comprises, for each of pluralregions on a surface of said article, determining a wobble angle atwhich reflectance from said article is at a maximum.

C. The method A in which said assessing comprises capturing lightreflected from a region of a card towards a first direction, and towardsa second direction, and determining therefrom a wobble angle at whichreflectance from said region is at a maximum.

D. The method C in which said capturing comprises capturing light usinga 2D optical sensor in a camera-equipped mobile phone.

E. The method A that further comprises illuminating the article withdiffuse illumination.

F. The method A, that includes assessing directional albedo datarelating to a surface of an identification document.

G. A method of issuing driver licenses that includes fabricating alicense, and providing same to an applicant, further characterized by,prior to said providing, collecting a unique set of physical measurementdata from each license, and storing same in a database, wherein thestored data can later be consulted to identify said license.

H. The method G wherein said collecting comprises capturing informationrelated to topographic minutiae from each license.

I. The method G wherein said collecting comprises capturing lightreflected from a region of a license towards a first direction, andtowards a second direction, and determining therefrom a wobble angle atwhich reflectance from said region is at a maximum.

J. A method that includes assessing directional albedo data relating toa first article, and determining—by reference to said data andearlier-assessed albedo data relating to a second article, whether thefirst article is the second article.

K. The method J that includes: reading a machine-readable identifierfrom said first article; by reference to said machine-readableidentifier, identifying a set of earlier-assessed albedo data; andchecking said identified set of earlier-assessed albedo data againstalbedo data relating to the first article.

L. The method K that includes capturing 2D image data from the firstarticle, and determining therefrom both said directional albedo data,and said machine-readable identifier.

M. A method comprising: assessing directional albedo data relating to anarticle; and checking data corresponding to said assessment againstearlier-stored data, to determine an identity of said article.

N. A method comprising: for each of plural areas on an article, sensingenergy reflected therefrom in plural different directions, to determinea direction of maximum reflectance; and by reference to data therebygathered, identifying a reference set of earlier-stored datacorresponding thereto, and thereby identifying the article.

O. The method N that includes sensing energy reflected from anidentification document, and identifying the document.

P. A method comprising: sensing an attribute from an object, from firstand second different directions; producing data therefrom; and byreference to said produced data, in conjunction with earlier-storeddata, making a determination concerning said object.

Q. The method P in which the object comprises several regions, and saidsensing comprises sensing light reflected from one of said regions in afirst, and in a second, direction.

R. The method P that includes making a determination concerning anidentification of said object.

S. The method P that comprises sensing said attribute from anidentification document.

T. A method comprising: causing relative movement between an object(e.g., an identity object) and a camera-equipped cell phone; capturingplural successive images of the object from different viewpoints saidcamera-equipped mobile phone; and by reference to data therebycollected, making a determination concerning said object.

U. The method T that includes making a determination concerningidentification of said object.

V. A method comprising: waving an article before a 2D optical sensordevice along a random track; and by reference to data produced by saiddevice, making a determination concerning said article.

W. A method comprising: causing relative movement between an identitycard and a 2D optical sensor; capturing plural frames of image dataduring said movement; determining position data for said card for eachof plural frames; decoding a steganographically encoded data signalconveyed by said card; and processing said captured image data, inconjunction with said position data, to determine a physical functionassociated with said card.

X. The method W that further comprises checking previously-storedphysical function data with said determined physical function, forcorrespondence.

Y. The method X that includes, by reference to the decoded data signal,identifying a record in a database purportedly corresponding to saidcard, and obtaining said previously-stored physical function datatherefrom.

Z. The method W wherein said determining position data proceeds byreference to a steganographically encoded reference signal conveyed bysaid card

AA. The method W wherein said physical function has a directionalaspect.

BB. A method of issuing driver licenses that includes fabricating alicense, and providing the fabricated license to an applicant, furthercharacterized by processing the license prior to said providing, so asto deliberately impart thereto a random physical feature, and storingdata related to said feature for later use.

CC. A method comprising: receiving a card, the card having a set ofplural physical features that, in the aggregate, define an initialstate; applying a stimulus to the card, said stimulus causing plural ofsaid physical features to change, thereby defining a second state ofsaid set of features; and storing data related to said second state.

DD. The method CC that further includes using said second state of saidset of features in connection with a cryptographic function.

EE. The method CC wherein the card comprises plural regions, and saidset of features comprises a parameter associated with each of saidregions.

FF. The method CC wherein said parameters comprise directional albedoparameters.

GG. The method CC wherein said stimulus comprises a physical pressureforce, which changes a surface topology of said card.

HH. The method CC wherein said stimulus comprises localized applicationof electromagnetic energy to certain areas of said card.

II. The method HH wherein said stimulus comprises laser irradiation,applied to said card from a first direction, and further irradiation,applied to said card from a second, different, direction.

JJ. The method HH wherein said stimulus comprises laser irradiation,applied to first positions on said card for a first interval of time,and applied to second positions on said card for a second, longerinterval of time.

KK. The method CC wherein said stimulus comprises a liquid sprayed onsaid card.

LL. The method CC wherein said applying has a random aspect.

MM. The method CC wherein said applying comprises heating the card toreturn the card to an initial state, and thereafter applying a furtherstimulus to the card, to cause said set of physical features to changeto the second state.

NN. A method of customizing a card comprising: applying laserirradiation to first regions of said card from a first direction; andapplying laser irradiation to second regions of said card from a second,different, direction.

OO. A card construction for use with ID documents, the card comprising asubstrate and plural layers, at least one of said layers comprisingquantum dots, and one of said layers comprising a photoreactive materialwhose transmissivity is changeable by application of stimulus thereto.

PP. An apparatus comprising: a card positioning system, by which an IDcard can be positioned at a known location; an optical imaging system,arranged to capture optical data from a card positioned at said locationfrom plural different directions; and a processor adapted to processdata received from said plural optical sensors, and determinetherefrom—for each of plural areas on the surface of said card—a wobbleangle associated therewith.

QQ. The apparatus PP wherein said optical imaging system comprisesplural 2D optical sensors, each with an associated lens, positioned tocapture optical information from a card at said location from pluraldifferent directions.

RR. In a method of compiling a database useful by an issuer of identitycredentials, the method including storing—for each of plural issuedcredentials—a name of a credentialed person, and an image of suchperson, an improvement that includes also storing physical minutiaeinformation unique to said issued credential.

SS. A method comprising: capturing image data from an identificationdocument; and by reference to information previously known about saiddocument, characterizing a position of said document in at least threedimensions.

TT. The method SS that includes, by reference to steganographicallyencoded calibration data conveyed by said document, characterizing aposition of said document in at least three dimensions.

UU. The method SS wherein said dimensions comprise at least tip, tilt,and rotation angles.

VV. A card construction for use with ID documents, the card comprising asubstrate and at least one layer thereon, characterized in that a layerthereof includes a material responsive to stimulus to change an opticalproperty thereof.

Having described and illustrated various principles of our work byreference to particular examples, it should be apparent that thedetailed technology can be modified in arrangement and detail withoutdeparting from such principles. Accordingly, we claim all suchembodiments as come within the scope and spirit of the following claimsand equivalents thereto.

1. A method comprising: causing relative movement between an object anda camera-equipped cell phone; capturing plural successive images of theobject from different viewpoints said camera-equipped mobile phone; byreference to data thereby collected, determining a physical functionassociated with the object; and making a determination concerning saidobject, by reference to said determined physical function andpreviously-stored physical function data; wherein said determination ismade independently of information about positioning of any illuminationsource relative to the object.
 2. The method of claim 1 wherein saidobject comprises an identity document.
 3. The method of claim 1 thatincludes making a determination concerning identification of saidobject.
 4. A method comprising: waving an article before a 2D opticalsensor device along a random track; by reference to data produced bysaid device, determining a physical function associated with the object;and making a determination concerning said article, by reference to saiddetermined physical function and previously-stored physical functiondata; wherein said determination is made independently of informationabout positioning of any illumination source relative to the article. 5.A method comprising: causing relative movement between an identity cardand a 2D optical sensor; capturing plural frames of image data duringsaid movement, said frames including image data corresponding to saididentity card; determining relative position data for each of pluralframes; decoding a steganographically encoded data signal conveyed bysaid card; processing said captured image data, in conjunction with saidposition data, to determine a physical function associated with saidcard; and checking previously-stored physical function data with saiddetermined physical function, for correspondence; wherein saidprocessing is done independently of information about positioning of anyillumination source relative to the identity card.
 6. The method ofclaim 5 that includes, by reference to the decoded data signal,identifying a record in a database purportedly corresponding to saidcard, and obtaining said previously-stored physical function datatherefrom.
 7. The method of claim 5 wherein said determining positiondata proceeds by reference to a steganographically encoded referencesignal conveyed by said card.
 8. The method of claim 5 wherein saidphysical function has a directional aspect.
 9. The method of claim 5that further comprises checking previously-stored physical function datawith said determined physical function, for correlation.
 10. A methodthat includes: causing relative movement between an object and a 2Doptical sensor; generating object-related physical function data, saidgenerating including capturing a frame of image data during saidmovement, said frame of image data including at least some image datacorresponding to said object, and determining relative position dataassociated with said captured frame of image data; sensing amachine-readable identifier from the object; accessing a remote datastore containing previously-stored physical function data associatedwith said sensed identifier; and checking the previously-stored physicalfunction data from said remote data store with said object-relatedphysical function data for correspondence; wherein said method proceedsindependently of information about positioning of any illuminationsource relative to the object.
 11. The method of claim 10 that includeschecking the data from said remote data store with said object-relateddata for correlation.
 12. The method of claim 10 in which the objectcomprises an identity card.
 13. The method of claim 10 in which said actof generating object-related data includes capturing plural frames ofimage data during said movement, and determining relative position dataassociated with each of said captured frames.
 14. The method of claim 10wherein said capturing a frame of image data comprises capturing with a2D optical sensor in a camera-equipped telephone.
 15. The method ofclaim 1 wherein said determination is made by a processor, configured inaccordance with software instructions, and proceeds with reference toinformation related to surface topology of the object.
 16. The method ofclaim 4 wherein said determination is made by a processor, configured inaccordance with software instructions, and proceeds with reference toinformation related to surface topology of the article.
 17. The methodof claim 5 wherein said processing is performed by a processor,configured in accordance with software instructions, and proceeds withreference to information related to surface topology of the identitycard.
 18. The method of claim 10 wherein said generating is performed bya processor, configured in accordance with software instructions, andproceeds with reference to information related to surface topology ofthe object.
 19. A computer readable medium containing non-transitoryinstructions for causing a process programmed thereby to perform thefollowing acts: control the capturing of image data from an article;process the image data to obtain position data relating to positioningof the article; by reference to the image data and the position data,determine physical function data associated with the object; and make adetermination concerning the authenticity of the article by reference tosaid determined physical function data and previously-stored physicalfunction data, said determination being made independently ofinformation about positioning of any illumination source relative to thearticle.
 20. A method that includes: collecting plural frames of imagedata of an object, each with a different perspective; step for checkinga match between said object to a reference object, through use ofpreviously-stored physical function data, and independently ofinformation about positioning of any illumination source relative to thearticle; and providing an indication to a user if a match is found. 21.The method of claim 20 wherein said step comprises performing acorrelation operation using a computer.